Dynamic machine learning decision threshold for resource allocation

ABSTRACT

Methods and apparatuses are provided for dynamic machine learning decision threshold for resource allocation/de-allocation. In one embodiment, a network node includes processing circuitry configured to cause the network node to dynamically adjust an allocation decision threshold; and determine whether to allocate at least one radio resource based at least in part on the allocation decision threshold. In one embodiment, a network node includes processing circuitry configured to cause the network node to dynamically adjust a de-allocation decision threshold; and determine whether to de-allocate at least one radio resource based at least in part on the de-allocation decision threshold.

TECHNICAL FIELD

The present disclosure relates wireless communications and, inparticular, to dynamic machine learning decision threshold for resourceallocation/de-allocation.

BACKGROUND

In a wireless network, there is a general problem of assigning a limitednumber of radio resources in a given system to user devices (e.g.,wireless devices and/or user equipments). Techniques for moreefficiently allocating such radio resources to user devices are beingconsidered.

SUMMARY

Some embodiments of the present disclosure advantageously providemethods, apparatuses and systems related to using one or more dynamicmachine learning decision thresholds for resourceallocation/de-allocation.

According to one aspect of the present disclosure, a method implementedin a network node is provided. The method includes dynamically adjustingan allocation decision threshold; and determining whether to allocate atleast one radio resource based at least in part on the allocationdecision threshold.

In some embodiments of this aspect, determining whether to allocate theat least one radio resource further includes determining whether toallocate the at least one radio resource for a sounding referencesignal, SRS, based at least in part on the allocation decisionthreshold. In some embodiments of this aspect, dynamically adjusting theallocation decision threshold includes dynamically adjusting theallocation decision threshold to achieve a target allocation. In someembodiments of this aspect, the target allocation comprises a targetprobability that an event will occur. In some embodiments of thisaspect, the target allocation is based at least in part on at least oneof: a target allocation error; a cost associated with allocating the atleast one radio resource; and a number of radio resource control, RRC,reconfigurations associated with allocating the at least one radioresource.

In some embodiments of this aspect, the target probability is apredetermined target probability that the event will occur. In someembodiments of this aspect, the event is one of: that a radio resourceis unavailable for allocation to a wireless device; and that the radioresource is unavailable for allocation to the wireless device while anestimated benefit to the wireless device is greater than or equal to theallocation decision threshold. In some embodiments of this aspect,dynamically adjusting the allocation decision threshold includesdetermining whether there is at least one radio resource that isavailable for the allocation to a wireless device; increasing theallocation decision threshold by at least one step up parameter whenthere is at least one radio resource that is available for theallocation to the wireless device; and decreasing the allocationdecision threshold by at least one step down parameter when there is anunavailability of at least one radio resource for the allocation to thewireless device.

In some embodiments of this aspect, dynamically adjusting the allocationdecision threshold includes determining whether there is at least oneradio resource that is available for the allocation to a wirelessdevice; increasing the allocation decision threshold by at least onestep up parameter when there is at least one radio resource that isavailable for the allocation to the wireless device and an estimatedbenefit to the wireless device is greater than or equal to theallocation decision threshold; and decreasing the allocation decisionthreshold by at least one step down parameter when there is anunavailability of at least one radio resource for the allocation to thewireless device.

In some embodiments of this aspect, a size of at least one of the stepup parameter and the step down parameter is based at least in part on atarget allocation. In some embodiments of this aspect, determiningwhether to allocate the at least one radio resource based at least inpart on the allocation decision threshold includes estimating a benefitof allocating the at least one radio resource to the wireless device;comparing the estimated benefit to the allocation decision threshold;and one of allocating and not allocating the at least one radio resourceto the wireless device based at least in part on the comparison of theestimated benefit to the allocation decision threshold. In someembodiments of this aspect, estimating the benefit of allocating the atleast one radio resource to the wireless device further includesestimating the benefit to the wireless device based at least in part onat least one of: a total of data transmitted in a downlink channel tothe wireless device; a total of time that the wireless device has beenactive in a system of the network node; a downlink inactivity time; andusing a machine learning algorithm.

In some embodiments of this aspect, the method further includes one ofallocating and not allocating the at least one radio resource to thewireless device based on the determination. In some embodiments of thisaspect, determining whether to allocate the at least one radio resourcebased at least in part on the allocation decision threshold includesusing the dynamically adjusted allocation decision threshold to controlan output of a binary classification system, the binary classificationsystem configured to determine whether to allocate the at least oneradio resource to the wireless device.

According to another aspect of the present disclosure, a methodimplemented in a network node is provided. The method includesdynamically adjusting a de-allocation decision threshold; anddetermining whether to de-allocate at least one radio resource based atleast in part on the de-allocation decision threshold.

In some embodiments of this aspect, determining whether to de-allocatethe at least one radio resource further includes determining whether tode-allocate the at least one radio resource for a sounding referencesignal, SRS, based at least in part on the allocation decisionthreshold. In some embodiments of this aspect, dynamically adjusting thede-allocation decision threshold includes dynamically adjusting thede-allocation decision threshold to achieve a target de-allocation. Insome embodiments of this aspect, the target de-allocation includes atarget probability that an event will occur.

In some embodiments of this aspect, the target de-allocation is based atleast in part on at least one of: a target de-allocation error; a costassociated with de-allocating the at least one radio resource; and anumber of radio resource control, RRC, reconfigurations associated withde-allocating the at least one radio resource. In some embodiments ofthis aspect, the target probability is a predetermined targetprobability associated with at least one of: a probability ofde-allocating resources to a wireless device; and a probability ofde-allocating resources to the wireless device and a same wirelessdevice is subsequently allocated resources.

In some embodiments of this aspect, determining whether to de-allocatethe at least one radio resource based at least in part on thede-allocation decision threshold includes estimating a non-benefit ofde-allocating the at least one radio resource to the wireless device;comparing the estimated non-benefit to the de-allocation decisionthreshold; and one of de-allocating and not de-allocating the at leastone radio resource to the wireless device based at least in part on thecomparison of the estimated non-benefit to the de-allocation decisionthreshold.

In some embodiments of this aspect, dynamically adjusting thede-allocation decision threshold further includes increasing thede-allocation decision threshold by at least one step up parameter whenthe one of the de-allocating and not de-allocating is de-allocating; anddecreasing the de-allocation decision threshold by at least one stepdown parameter when the one of the de-allocating and not de-allocatingis not deallocating.

In some embodiments of this aspect, dynamically adjusting thede-allocation decision threshold further includes decreasing thede-allocation decision threshold by at least one step down parameterwhen the one of the de-allocating and not deallocating is de-allocating;and increasing the de-allocation decision threshold by at least one stepup parameter and at least one step down parameter when the one of thede-allocating and not de-allocating is not de-allocating and is furthera re-allocation. In some embodiments of this aspect, a size of at leastone of the step up parameter and the step down parameter is based atleast in part on a target de-allocation. In some embodiments of thisaspect, estimating the non-benefit of de-allocating the at least oneradio resource to the wireless device includes estimating thenon-benefit to the wireless device based at least in part on at leastone of: a total of data transmitted in a downlink channel to thewireless device; a total of time that the wireless device has beenactive in a system of the network node; a downlink inactivity time; andusing a machine learning algorithm. In some embodiments of this aspect,the method further includes one of de-allocating and not de-allocatingthe at least one radio resource to the wireless device based on thedetermination. In some embodiments of this aspect, determining whetherto de-allocate the at least one radio resource based at least in part onthe de-allocation decision threshold includes using the dynamicallyadjusted de-allocation decision threshold to control an output of abinary classification system, the binary classification systemconfigured to determine whether to deallocate the at least one radioresource to the wireless device.

According to yet another aspect of the present disclosure, a networknode is provided. The network node includes processing circuitry. Theprocessing circuitry is configured to cause the network node todynamically adjust an allocation decision threshold; and determinewhether to allocate at least one radio resource based at least in parton the allocation decision threshold.

In some embodiments of this aspect, the processing circuitry isconfigured to determine whether to allocate the at least one radioresource by being configured to determine whether to allocate the atleast one radio resource for a sounding reference signal, SRS, based atleast in part on the allocation decision threshold. In some embodimentsof this aspect, the processing circuitry is configured to dynamicallyadjust the allocation decision threshold by being configured todynamically adjust the allocation decision threshold to achieve a targetallocation. In some embodiments of this aspect, the target allocationcomprises a target probability that an event will occur.

In some embodiments of this aspect, the target allocation is based atleast in part on at least one of: a target allocation error; a costassociated with allocating the at least one radio resource; and a numberof radio resource control, RRC, reconfigurations associated withallocating the at least one radio resource. In some embodiments of thisaspect, the target probability is a predetermined target probabilitythat the event will occur. In some embodiments of this aspect, the eventis one of: that a radio resource is unavailable for allocation to awireless device; and that the radio resource is unavailable forallocation to the wireless device while an estimated benefit to thewireless device is greater than or equal to the allocation decisionthreshold.

In some embodiments of this aspect, the processing circuitry isconfigured to dynamically adjust the allocation decision threshold bybeing configured to determine whether there is at least one radioresource that is available for the allocation to a wireless device;increase the allocation decision threshold by at least one step upparameter when there is at least one radio resource that is availablefor the allocation to the wireless device; and decrease the allocationdecision threshold by at least one step down parameter when there is anunavailability of at least one radio resource for the allocation to thewireless device.

In some embodiments of this aspect, the processing circuitry isconfigured to dynamically adjust the allocation decision threshold bybeing configured to determine whether there is at least one radioresource that is available for the allocation to a wireless device;increase the allocation decision threshold by at least one step upparameter when there is at least one radio resource that is availablefor the allocation to the wireless device and an estimated benefit tothe wireless device is greater than or equal to the allocation decisionthreshold; and decrease the allocation decision threshold by at leastone step down parameter when there is an unavailability of at least oneradio resource for the allocation to the wireless device.

In some embodiments of this aspect, a size of at least one of the stepup parameter and the step down parameter is based at least in part on atarget allocation. In some embodiments of this aspect, the processingcircuitry is configured to determine whether to allocate the at leastone radio resource based at least in part on the allocation decisionthreshold by being configured to: estimate a benefit of allocating theat least one radio resource to the wireless device; compare theestimated benefit to the allocation decision threshold; and one ofallocate and not allocate the at least one radio resource to thewireless device based at least in part on the comparison of theestimated benefit to the allocation decision threshold. In someembodiments of this aspect, the processing circuitry is configured toestimate the benefit of allocating the at least one radio resource tothe wireless device by being configured to: estimate the benefit to thewireless device based at least in part on at least one of: a total ofdata transmitted in a downlink channel to the wireless device; a totalof time that the wireless device has been active in a system of thenetwork node; a downlink inactivity time; and using a machine learningalgorithm. In some embodiments of this aspect, the processing circuitryis further configured to one of allocate and not allocate the at leastone radio resource to the wireless device based on the determination.

In some embodiments of this aspect, the processing circuitry isconfigured to determine whether to allocate the at least one radioresource based at least in part on the allocation decision threshold bybeing configured to use the dynamically adjusted allocation decisionthreshold to control an output of a binary classification system, thebinary classification system configured to determine whether to allocatethe at least one radio resource to the wireless device.

According to another aspect of the present disclosure, a network node isprovided. The network node includes processing circuitry. The processingcircuitry is configured to cause the network node to dynamically adjusta de-allocation decision threshold; and determine whether to de-allocateat least one radio resource based at least in part on the de-allocationdecision threshold.

In some embodiments of this aspect, the processing circuitry isconfigured to determine whether to de-allocate the at least one radioresource by being configured to determine whether to de-allocate the atleast one radio resource for a sounding reference signal, SRS, based atleast in part on the allocation decision threshold. In some embodimentsof this aspect, the processing circuitry is configured to dynamicallyadjust the de-allocation decision threshold by being configured todynamically adjust the de-allocation decision threshold to achieve atarget de-allocation. In some embodiments of this aspect, the targetde-allocation includes a target probability that an event will occur.

In some embodiments of this aspect, the target de-allocation is based atleast in part on at least one of: a target de-allocation error; a costassociated with de-allocating the at least one radio resource; and anumber of radio resource control, RRC, reconfigurations associated withde-allocating the at least one radio resource. In some embodiments ofthis aspect, the target probability is a predetermined targetprobability associated with at least one of: a probability ofde-allocating resources to a wireless device; and a probability ofde-allocating resources to the wireless device and a same wirelessdevice is subsequently allocated resources.

In some embodiments of this aspect, the processing circuitry isconfigured to determine whether to de-allocate the at least one radioresource based at least in part on the de-allocation decision thresholdby being configured to estimate a non-benefit of de-allocating the atleast one radio resource to the wireless device; compare the estimatednon-benefit to the de-allocation decision threshold; and one ofde-allocate and not de-allocate the at least one radio resource to thewireless device based at least in part on the comparison of theestimated non-benefit to the de-allocation decision threshold. In someembodiments of this aspect, the processing circuitry is configured todynamically adjust the de-allocation decision threshold by beingconfigured to increase the de-allocation decision threshold by at leastone step up parameter when the one of the de-allocating and notde-allocating is de-allocating; and decrease the de-allocation decisionthreshold by at least one step down parameter when the one of thede-allocating and not de-allocating is not de-allocating.

In some embodiments of this aspect, the processing circuitry isconfigured to dynamically adjust the de-allocation decision threshold bybeing configured to decrease the de-allocation decision threshold by atleast one step down parameter when the one of the de-allocating and notde-allocating is de-allocating; and increase the de-allocation decisionthreshold by at least one step up parameter and at least one step downparameter when the one of the de-allocating and not de-allocating is notdeallocating and is further a re-allocation. In some embodiments of thisaspect, a size of at least one of the step up parameter and the stepdown parameter is based at least in part on a target de-allocation.

In some embodiments of this aspect, the processing circuitry isconfigured to estimate the non-benefit of de-allocating the at least oneradio resource to the wireless device by being configured to estimatethe non-benefit to the wireless device based at least in part on atleast one of: a total of data transmitted in a downlink channel to thewireless device; a total of time that the wireless device has beenactive in a system of the network node; a downlink inactivity time; andusing a machine learning algorithm. In some embodiments of this aspect,the processing circuitry is further configured to one of de-allocate andnot de-allocate the at least one radio resource to the wireless devicebased on the determination. In some embodiments of this aspect, theprocessing circuitry is configured to determine whether to de-allocatethe at least one radio resource based at least in part on thede-allocation decision threshold by being configured to use thedynamically adjusted de-allocation decision threshold to control anoutput of a binary classification system, the binary classificationsystem configured to determine whether to de-allocate the at least oneradio resource to the wireless device.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and theattendant advantages and features thereof, will be more readilyunderstood by reference to the following detailed description whenconsidered in conjunction with the accompanying drawings wherein:

FIG. 1 is a schematic diagram of an example network architectureillustrating a communication system according to the principles in thepresent disclosure;

FIG. 2 is a block diagram of a network node in communication with awireless device over an at least partially wireless connection accordingto some embodiments of the present disclosure;

FIG. 3 is a flowchart of an example method for a network node forallocating resources according to one embodiment of the presentdisclosure;

FIG. 4 is a flowchart of an example method for a network node fordeallocating resources according to one embodiment of the presentdisclosure; and

FIG. 5 is a flow chart illustrating an example method of dynamicallyadjusting a decision threshold for allocating and de-allocatingresources according to one embodiment of the present disclosure.

DETAILED DESCRIPTION

As discussed above, there is a general problem of assigning a limitednumber of radio resources in a system to user devices (e.g., wirelessdevices and/or user equipments). Such problems may generally beassociated with one or more of the following:

-   -   user devices arrive at an unknown arrival rate to the system;    -   a user device may receive a benefit from having a resource        allocated to such user;    -   a user device may be allocated at most one resource e.g., for a        particular signal;    -   a user device may be allocated and/or de-allocated the resource        by the system at any time the user device is active (though,        there is a cost for allocating and deallocating resources);    -   a user device enters the system, and stays active for a period        of time, that is unknown to the system (after this period, the        user device exits the system and returns the resource that the        user device was allocated if any); and    -   a system may be considered to receive a benefit (>=0) by        allocating a resource to a user device. The benefit may be        governed by one or more of the following attributes:        -   For a given user device, the benefit may be the same            regardless of the resource that was chosen to be allocated            to the user device. In other words, all resources are equal            from the user device point of view.        -   For a given resource, the benefit of allocating the resource            to user device i can be different from allocating the            resource to user device j.        -   The exact benefit of a resource to a user device may not be            readily available to the system. However, the system may            have access to an estimator that outputs a quantity,            p(user), which predicts the likely benefit of the user            device if the user device is allocated a resource. The            estimator may strive to output higher values when higher            benefits are likely, but the estimator is not guaranteed to            achieve that all the time. In other words, the estimator may            have an inherent inaccuracy in estimating the benefit.        -   The benefit of a resource to a user device is a            non-decreasing function of the time the user device is            assigned the resource. The benefit is by definition 0 when            the user device is not assigned a resource for the entire            active period.        -   The system benefit may be considered a total of benefits            received by the system's user devices.

One goal of the system may be to allocate its limited resources tomaximize its long-term average benefits, which may be defined as the sumof all benefits received by the user devices divided by a time window(T), as T goes to infinity. One application of the problem describedabove is the allocation of limited periodic sounding reference signals(SRS) resources in Third Generation Partnership Project Long TermEvolution (LTE) and/or 3GPP New Radio (NR), also called 5G networks. Forexample, each allocation or de-allocation of periodic SRS resourcesinvolves radio resource control (RRC) configuration/reconfigurationsignaling, which the network may attempt to minimize. Each user devicehaving been allocated an SRS resource transmits a sounding referencesignal as specified by the RRC messages. Transmission of SRS allows thenetwork to obtain more accurate knowledge of the physical uplinkchannel. If downlink-uplink reciprocity holds, e.g., as intime-division-duplexing (TDD) scenarios, the network can also use theSRS to estimate the physical downlink channel. Such accurate estimationof the channel allows the network to serve user devices better, ascompared to existing networks without SRS, by designing betterbeamforming and improving link adaptations and power control, which canlead to higher throughputs for such user devices. Since there arelimited number of periodic SRS resources, the network may strive toallocate such resources to user devices who will likely benefit the mostfrom SRS resources. For example, the network may be designed and/orconfigured with a policy that strives to allocate SRS resources to userdevices (e.g., wireless devices and/or user equipments) that downloadthe largest amount of data. The network may have an observation windowin time, to observe the user device traffic, and then to decide whetherthe user device is likely to be a ‘heavy user,’ which may indicate theuser device is likely to receive a higher benefit from being allocatedan SRS resource (as compared to a ‘light user’, which may be e.g., auser device that does not download large amounts of data).Alternatively, the network may asynchronously react to an event at anytime to determine whether the user device is likely to be associatedwith a heavy user.

Yet another application of the problem described above is when thecomputational power in the network is limited, such that advancedalgorithms cannot be executed for every user device. In suchapplication, the network may strive to apply advanced algorithms only tothe top user devices who will receive a largest benefit from suchalgorithms, as compared to other user devices.

A trivial way to assign the limited resources, e.g., in the aboveapplications is to assign resources in a first-come-first-serveapproach. This approach may work well if the number of active userdevices in the system is less or equal to the number of resources. Inthe general case, however, this approach can be quite inefficient.

Better approaches to assign the limited resources have been considered.For example, a machine learning classifying algorithm may be consideredto classify the user devices to ‘heavy’ or ‘light’ based on a predicteddata volume and/or data throughput. In particular, an observation windowmay be specified during which traffic characteristics may be observed.At the end of the observation window, a machine learning algorithm mayuse the traffic characteristics to derive input features which may thenbe fed as inputs into a classification algorithm that predicts whether auser device is ‘heavy’ or ‘light’ user device. The user device may beassigned one of the limited resources if and only if such user device ispredicted to be a heavy user device.

However, such approaches do not take into consideration the long-termload of a system in allocating the limited resources to users, whichreduces the efficacy of such solutions.

Some embodiments of the present disclosure provide for dynamicthresholds that are used in deciding/determining whether the user deviceshould be allocated and/or deallocated a resource. In particular, insome embodiments, one or more of at least two decision thresholds,called herein as, allocTh and dellaocTh, may be used, such that:

-   -   a user device without a resource will be allocated a resource if        the user's estimated or predicted benefit at least meets (e.g.,        is greater-than or equal-to) an allocation threshold (allocTh);        and/or    -   a user device with a resource will be de-allocated the resource        if the user's estimated or predicted non-benefit at least meets        (e.g., is greater-than or equal-to) a de-allocation threshold        (deallocTh).

In some embodiments, one or more of the two thresholds are updateddynamically in order to achieve a predefined target probability of aparticular event, which is explained in more detail herein below.

Some embodiments may advantageously achieve higher long-term benefitscompared to existing schemes that do not adapt the decision criteria forallocating and deallocating resources. The techniques provided in thisdisclosure may be applied in the problem of allocating periodic SRSresources and achieve better throughput by allocating SRS resources touser devices with a predicted higher data volume. Alternatively, oradditionally, the techniques provided in this disclosure may be appliedto other resource allocation problems, as well, such as other signals,other types of reference signals, other channels and yet other types ofresources that can be allocated by a network/system for a user.

Before describing in detail exemplary embodiments, it is noted that theembodiments reside primarily in combinations of apparatus components andprocessing steps related to dynamic machine learning decision thresholdfor resource allocation. Accordingly, components have been representedwhere appropriate by conventional symbols in the drawings, showing onlythose specific details that are pertinent to understanding theembodiments so as not to obscure the disclosure with details that willbe readily apparent to those of ordinary skill in the art having thebenefit of the description herein. Like numbers refer to like elementsthroughout the description.

As used herein, relational terms, such as “first” and “second,” “top”and “bottom,” and the like, may be used solely to distinguish one entityor element from another entity or element without necessarily requiringor implying any physical or logical relationship or order between suchentities or elements. The terminology used herein is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the concepts described herein. As used herein, the singularforms “a”, “an” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will be furtherunderstood that the terms “comprises,” “comprising,” “includes” and/or“including” when used herein, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

In embodiments described herein, the joining term, “in communicationwith” and the like, may be used to indicate electrical or datacommunication, which may be accomplished by physical contact, induction,electromagnetic radiation, radio signaling, infrared signaling oroptical signaling, for example. One having ordinary skill in the artwill appreciate that multiple components may interoperate andmodifications and variations are possible of achieving the electricaland data communication.

In some embodiments described herein, the term “coupled,” “connected,”and the like, may be used herein to indicate a connection, although notnecessarily directly, and may include wired and/or wireless connections.

The term “network node” used herein can be any kind of network nodecomprised in a radio network which may further comprise any of basestation (BS), radio base station, base transceiver station (BTS), basestation controller (BSC), radio network controller (RNC), g Node B(gNB), evolved Node B (eNB or eNodeB), Node B, multi-standard radio(MSR) radio node such as MSR BS, multi-cell/multicast coordinationentity (MCE), integrated access and backhaul (IAB) node, relay node,donor node controlling relay, radio access point (AP), transmissionpoints, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head(RRH), baseband unit (BBU), a core network node (e.g., mobile managemententity (MME), self-organizing network (SON) node, a coordinating node,positioning node, MDT node, etc.), an external node (e.g., 3rd partynode, a node external to the current network), nodes in distributedantenna system (DAS), a spectrum access system (SAS) node, an elementmanagement system (EMS), etc. The network node may also comprise testequipment. The term “radio node” used herein may be used to also denotea wireless device (WD) such as a wireless device (WD) or a radio networknode.

In some embodiments, the non-limiting terms wireless device (WD) or auser equipment (UE) are used interchangeably. The WD herein can be anytype of wireless device capable of communicating with a network node oranother WD over radio signals, such as wireless device (WD). The WD mayalso be a radio communication device, target device, device to device(D2D) WD, machine type WD or WD capable of machine to machinecommunication (M2M), low-cost and/or low-complexity WD, a sensorequipped with WD, Tablet, mobile terminals, smart phone, laptop embeddedequipped (LEE), laptop mounted equipment (LME), USB dongles, CustomerPremises Equipment (CPE), an Internet of Things (IoT) device, or aNarrowband IoT (NB-IOT) device, etc.

Also, in some embodiments the generic term “radio network node” is used.It can be any kind of a radio network node which may comprise any ofbase station, radio base station, base transceiver station, base stationcontroller, network controller, RNC, evolved Node B (eNB), Node B, gNB,Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node,access point, radio access point, Remote Radio Unit (RRU) Remote RadioHead (RRH).

In some embodiments, the term “adjusting” may be considered to indicateincreasing or decreasing. In some embodiments, the term “dynamicallyadjusting” may be considered to indicate continuously adjusting as in,for example, an outer loop of an allocation control loop.

In some embodiments, the term “allocation” may be considered to refer toa WD being allocated one or more resources for a transmission, such as,for example, allocating a radio resource on a channel for a signal to betransmitted to or from the WD (e.g., time-frequency resource for SRS ona physical uplink channel). In some embodiments, a network node mayallocate resources by scheduling a WD and, for example, configuring theWD with the allocated resources via e.g., radio resource control (RRC)signaling in a higher layer and/or by signaling an indication of theallocated resources in MAC layer via, e.g., a MAC control element,and/or a physical layer via e.g., a grant in downlink controlinformation (DCI).

In some embodiments, the term “radio resource” is intended to indicate afrequency resource, a time resource, code resource, and/or spatialresource. The time resource may correspond to any type of physicalresource or radio resource expressed in terms of length of time.Examples of time resources are: symbol, time slot, subframe, radioframe, transmission time interval (TTI), interleaving time, etc. Thefrequency resource may correspond to one or more resource elements,subcarriers, resource blocks, bandwidth part and/or any other resourcesin the frequency domain. The radio resource may also indicate acombination of subcarriers, time slots, codes and/or spatial dimensions.

Even though the descriptions herein may be explained in the context ofone of a Downlink (DL) and an Uplink (UL) communication, it should beunderstood that the basic principles disclosed may also be applicable tothe other of the one of the DL and the UL communication. For DLcommunication, the network node is the transmitter and the receiver isthe WD. For the UL communication, the transmitter is the WD and thereceiver is the network node.

Although some the examples herein may be explained in the context of aWD being allocated radio resources on a physical channel for a periodicreference signal (e.g., SRS), it should be understood that theprinciples may also be applicable to other signals and other types ofresources or other channels.

In some embodiments, the allocated radio resource may be allocated for aparticular signal and on a particular channel. Signaling may generallycomprise one or more symbols and/or signals and/or messages. A signalmay comprise or represent one or more bits. An indication may representsignaling, and/or be implemented as a signal, or as a plurality ofsignals. One or more signals may be included in and/or represented by amessage. Signaling, in particular control signaling, may comprise aplurality of signals and/or messages, which may be transmitted ondifferent carriers and/or be associated to different signalingprocesses, e.g. representing and/or pertaining to one or more suchprocesses and/or corresponding information. An indication may comprisesignaling, and/or a plurality of signals and/or messages and/or may becomprised therein, which may be transmitted on different carriers and/orbe associated to different acknowledgement signaling processes, e.g.representing and/or pertaining to one or more such processes. Signalingassociated to a channel may be transmitted such that representssignaling and/or information for that channel, and/or that the signalingis interpreted by the transmitter and/or receiver to belong to thatchannel. Such signaling may generally comply with transmissionparameters and/or format/s for the channel.

A channel may generally be a logical, transport or physical channel. Achannel may comprise and/or be arranged on one or more carriers, inparticular a plurality of subcarriers. A channel carrying and/or forcarrying control signaling/control information may be considered acontrol channel, in particular if it is a physical layer channel and/orif it carries control plane information. Analogously, a channel carryingand/or for carrying data signaling/user information may be considered adata channel, in particular if it is a physical layer channel and/or ifit carries user plane information. A channel may be defined for aspecific communication direction, or for two complementary communicationdirections (e.g., UL and DL, or sidelink in two directions), in whichcase it may be considered to have at least two component channels, onefor each direction. Examples of channels comprise a channel for lowlatency and/or high reliability transmission, in particular a channelfor Ultra-Reliable Low Latency Communication (URLLC), which may be forcontrol and/or data. In some embodiments, the channel described hereinmay be an uplink channel and in further embodiments may be a physicaluplink shared channel (PUSCH) or a physical uplink control channel(PUCCH). In some embodiments, the channel may be a downlink channel,such as, a physical downlink control channel (PDCCH) or a physicaldownlink shared channel (PDSCH).

Transmitting in downlink may pertain to transmission from the network ornetwork node to the terminal. The terminal may be considered the WD orUE. Transmitting in uplink may pertain to transmission from the terminalto the network or network node. Transmitting in sidelink may pertain to(direct) transmission from one terminal to another. Uplink, downlink andsidelink (e.g., sidelink transmission and reception) may be consideredcommunication directions. In some variants, uplink and downlink may alsobe used to described wireless communication between network nodes, e.g.for wireless backhaul and/or relay communication and/or (wireless)network communication for example between base stations or similarnetwork nodes, in particular communication terminating at such. It maybe considered that backhaul and/or relay communication and/or networkcommunication is implemented as a form of sidelink or uplinkcommunication or similar thereto.

Note that although terminology from one particular wireless system, suchas, for example, 3GPP LTE and/or New Radio (NR), may be used in thisdisclosure, this should not be seen as limiting the scope of thedisclosure to only the aforementioned system. Other wireless systems,including without limitation Wide Band Code Division Multiple Access(WCDMA), Worldwide Interoperability for Microwave Access (WiMax), UltraMobile Broadband (UMB) and Global System for Mobile Communications(GSM), may also benefit from exploiting the ideas covered within thisdisclosure.

Note further, that functions described herein as being performed by awireless device or a network node may be distributed over a plurality ofwireless devices and/or network nodes. In other words, it iscontemplated that the functions of the network node and wireless devicedescribed herein are not limited to performance by a single physicaldevice and, in fact, can be distributed among several physical devices.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure belongs. It willbe further understood that terms used herein should be interpreted ashaving a meaning that is consistent with their meaning in the context ofthis specification and the relevant art and will not be interpreted inan idealized or overly formal sense unless expressly so defined herein.

Some embodiments provide arrangements for using one or more dynamicmachine learning decision thresholds for resourceallocation/de-allocation.

Referring now to the drawing figures, in which like elements arereferred to by like reference numerals, there is shown in FIG. 1 aschematic diagram of a communication system 10, according to anembodiment, such as a 3GPP-type cellular network that may supportstandards such as LTE and/or NR (5G), which comprises an access network12, such as a radio access network, and a core network 14. The accessnetwork 12 comprises a plurality of network nodes 16 a, 16 b, 16 c(referred to collectively as network nodes 16), such as NB s, eNBs, gNBsor other types of wireless access points, each defining a correspondingcoverage area 18 a, 18 b, 18 c (referred to collectively as coverageareas 18). Each network node 16 a, 16 b, 16 c is connectable to the corenetwork 14 over a wired or wireless connection 20. A first wirelessdevice (WD) 22 a located in coverage area 18 a is configured towirelessly connect to, or be paged by, the corresponding network node 16a. A second WD 22 b in coverage area 18 b is wirelessly connectable tothe corresponding network node 16 b. While a plurality of WDs 22 a, 22 b(collectively referred to as wireless devices 22) are illustrated inthis example, the disclosed embodiments are equally applicable to asituation where a sole WD is in the coverage area or where a sole WD isconnecting to the corresponding network node 16. Note that although onlytwo WDs 22 and three network nodes 16 are shown for convenience, thecommunication system may include many more WDs 22 and network nodes 16.

Also, it is contemplated that a WD 22 can be in simultaneouscommunication and/or configured to separately communicate with more thanone network node 16 and more than one type of network node 16. Forexample, a WD 22 can have dual connectivity with a network node 16 thatsupports LTE and the same or a different network node 16 that supportsNR. As an example, WD 22 can be in communication with an eNB forLTE/E-UTRAN and a gNB for NR/NG-RAN.

A network node 16 is configured to include an allocation unit 24 whichis configured to cause the network node to dynamically adjust anallocation decision threshold; and determine whether to allocate atleast one radio resource based at least in part on the allocationdecision threshold. In some embodiments, a network node 16 is configuredto include a de-allocation unit 26 which is configured to cause thenetwork node to dynamically adjust a de-allocation decision threshold;and determine whether to de-allocate at least one radio resource basedat least in part on the de-allocation decision threshold.

Example implementations, in accordance with an embodiment, of the WD 22and network node 16 discussed in the preceding paragraphs will now bedescribed with reference to FIG. 2 .

The communication system 10 further includes a network node 16 providedin a communication system 10 and including hardware 27 enabling it tocommunicate with the WD 22. The hardware 27 may include a communicationinterface 28 for setting up and maintaining a wired or wirelessconnection with an interface of a different communication device of thecommunication system 10, as well as a radio interface 30 for setting upand maintaining at least a wireless connection 32 with a WD 22 locatedin a coverage area 18 served by the network node 16. The radio interface30 may be formed as or may include, for example, one or more RFtransmitters, one or more RF receivers, and/or one or more RFtransceivers.

In the embodiment shown, the hardware 27 of the network node 16 furtherincludes processing circuitry 34. The processing circuitry 34 mayinclude a processor 36 and a memory 38. In particular, in addition to orinstead of a processor, such as a central processing unit, and memory,the processing circuitry 34 may comprise integrated circuitry forprocessing and/or control, e.g., one or more processors and/or processorcores and/or FPGAs (Field Programmable Gate Array) and/or ASICs(Application Specific Integrated Circuitry) adapted to executeinstructions. The processor 36 may be configured to access (e.g., writeto and/or read from) the memory 38, which may comprise any kind ofvolatile and/or nonvolatile memory, e.g., cache and/or buffer memoryand/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/oroptical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the network node 16 further has software 40 stored internally in,for example, memory 38, or stored in external memory (e.g., database,storage array, network storage device, etc.) accessible by the networknode 16 via an external connection. The software 40 may be executable bythe processing circuitry 34. The processing circuitry 34 may beconfigured to control any of the methods and/or processes describedherein and/or to cause such methods, and/or processes to be performed,e.g., by network node 16. Processor 36 corresponds to one or moreprocessors 36 for performing network node 16 functions described herein.The memory 38 is configured to store data, programmatic software codeand/or other information described herein. In some embodiments, thesoftware 40 may include instructions that, when executed by theprocessor 36 and/or processing circuitry 34, causes the processor 36and/or processing circuitry 34 to perform the processes described hereinwith respect to network node 16. For example, processing circuitry 34 ofthe network node 16 may include allocation unit 24 and/or de-allocationunit 26 configured to perform network node methods discussed herein,such as the methods discussed with reference to FIGS. 3 and 4 as well asother figures.

The communication system 10 further includes the WD 22 already referredto. The WD 22 may have hardware 42 that may include a radio interface 44configured to set up and maintain a wireless connection 32 with anetwork node 16 serving a coverage area 18 in which the WD 22 iscurrently located. The radio interface 44 may be formed as or mayinclude, for example, one or more RF transmitters, one or more RFreceivers, and/or one or more RF transceivers.

The hardware 42 of the WD 22 further includes processing circuitry 46.The processing circuitry 46 may include a processor 48 and memory 50. Inparticular, in addition to or instead of a processor, such as a centralprocessing unit, and memory, the processing circuitry 46 may compriseintegrated circuitry for processing and/or control, e.g., one or moreprocessors and/or processor cores and/or FPGAs (Field Programmable GateArray) and/or ASICs (Application Specific Integrated Circuitry) adaptedto execute instructions. The processor 48 may be configured to access(e.g., write to and/or read from) memory 50, which may comprise any kindof volatile and/or nonvolatile memory, e.g., cache and/or buffer memoryand/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/oroptical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the WD 22 may further comprise software 52, which is stored in,for example, memory 50 at the WD 22, or stored in external memory (e.g.,database, storage array, network storage device, etc.) accessible by theWD 22. The software 52 may be executable by the processing circuitry 46.The software 52 may include a client application 54. The clientapplication 54 may be operable to provide a service to a human ornon-human user via the WD 22. The client application 54 may interactwith the user to generate the user data that it provides.

The processing circuitry 46 may be configured to control any of themethods and/or processes described herein and/or to cause such methods,and/or processes to be performed, e.g., by WD 22. The processor 48corresponds to one or more processors 48 for performing WD 22 functionsdescribed herein. The WD 22 includes memory 50 that is configured tostore data, programmatic software code and/or other informationdescribed herein. In some embodiments, the software 52 and/or the clientapplication 54 may include instructions that, when executed by theprocessor 48 and/or processing circuitry 46, causes the processor 48and/or processing circuitry 46 to perform the processes described hereinwith respect to WD 22. For example, the processing circuitry 46 of thewireless device 22 may be configured to use resources and/or receiveand/or transmit on radio resources (e.g., physical layer resources, suchas, physical downlink control channel, physical downlink shared channel,physical uplink control channel and/or physical uplink shared channel,etc.) that are allocated to the WD 22 using one or more of thetechniques disclosed herein.

In some embodiments, the inner workings of the network node 16 and WD22, may be as shown in FIG. 2 and independently, the surrounding networktopology may be that of FIG. 1 .

Although FIGS. 1 and 2 show various “units” such as allocation unit 24and de-allocation unit 26 as being within a processor, it iscontemplated that these units may be implemented such that a portion ofthe unit is stored in a corresponding memory within the processingcircuitry. In other words, the units may be implemented in hardware orin a combination of hardware and software within the processingcircuitry.

In addition, although FIGS. 1 and 2 show both allocation unit 24 andde-allocation unit 26 as being with the network node 16, it iscontemplated that the network node 16 may include only one of theseunits.

FIG. 3 is a flowchart of an exemplary process in a network node 16 forallocating resources using a dynamic decision threshold according tosome embodiments of the present disclosure. One or more Blocks and/orfunctions and/or methods performed by the network node 16 may beperformed by one or more elements of network node 16 such as byallocation unit 24 in processing circuitry 34, processor 36,communication interface 28, radio interface 30, etc. according to theexample method. The example method includes dynamically adjusting (BlockS100), such as allocation unit 24, processing circuitry 34, processor36, communication interface 28 and/or radio interface 30, an allocationdecision threshold. The method includes determining (Block S102), suchas allocation unit 24, processing circuitry 34, processor 36,communication interface 28 and/or radio interface 30, whether toallocate at least one radio resource based at least in part on theallocation decision threshold.

In some embodiments, determining whether to allocate the at least oneradio resource further includes determining, such as via allocation unit24, processing circuitry 34, processor 36, communication interface 28and/or radio interface 30, whether to allocate the at least one radioresource for a sounding reference signal, SRS, based at least in part onthe allocation decision threshold. In some embodiments, dynamicallyadjusting the allocation decision threshold includes dynamicallyadjusting, such as via allocation unit 24, processing circuitry 34,processor 36, communication interface 28 and/or radio interface 30, theallocation decision threshold to achieve a target allocation. In someembodiments, the target allocation comprises a target probability thatan event will occur.

In some embodiments, the target allocation is based at least in part onat least one of: a target allocation error; a cost associated withallocating the at least one radio resource; and a number of radioresource control, RRC, reconfigurations associated with allocating theat least one radio resource. In some embodiments, the target probabilityis a predetermined target probability that the event will occur, theevent being one of: that a radio resource is unavailable for allocationto a wireless device; and that the radio resource is unavailable forallocation to the wireless device while an estimated benefit to thewireless device is greater than or equal to the allocation decisionthreshold.

In some embodiments, dynamically adjusting the allocation decisionthreshold includes determining, such as via allocation unit 24,processing circuitry 34, processor 36, communication interface 28 and/orradio interface 30, whether there is at least one radio resource that isavailable for the allocation to a wireless device 22; increasing, suchas via allocation unit 24, processing circuitry 34, processor 36,communication interface 28 and/or radio interface 30, the allocationdecision threshold by at least one step up parameter when there is atleast one radio resource that is available for the allocation to thewireless device 22; and decreasing, such as via allocation unit 24,processing circuitry 34, processor 36, communication interface 28 and/orradio interface 30, the allocation decision threshold by at least onestep down parameter when there is an unavailability of at least oneradio resource for the allocation to the wireless device 22.

In some embodiments, dynamically adjusting the allocation decisionthreshold includes determining, such as via allocation unit 24,processing circuitry 34, processor 36, communication interface 28 and/orradio interface 30, whether there is at least one radio resource that isavailable for the allocation to a wireless device 22; increasing, suchas via allocation unit 24, processing circuitry 34, processor 36,communication interface 28 and/or radio interface 30, the allocationdecision threshold by at least one step up parameter when there is atleast one radio resource that is available for the allocation to thewireless device 22 and an estimated benefit to the wireless device 22 isgreater than or equal to the allocation decision threshold; anddecreasing, such as via allocation unit 24, processing circuitry 34,processor 36, communication interface 28 and/or radio interface 30, theallocation decision threshold by at least one step down parameter whenthere is an unavailability of at least one radio resource for theallocation to the wireless device 22.

In some embodiments, a size of at least one of the step up parameter andthe step down parameter is based at least in part on a targetallocation. In some embodiments, determining whether to allocate the atleast one radio resource based at least in part on the allocationdecision threshold includes estimating, such as via allocation unit 24,processing circuitry 34, processor 36, communication interface 28 and/orradio interface 30, a benefit of allocating the at least one radioresource to the wireless device; comparing, such as via allocation unit24, processing circuitry 34, processor 36, communication interface 28and/or radio interface 30, the estimated benefit to the allocationdecision threshold; and one of allocating and not allocating the atleast one radio resource to the wireless device 22 based at least inpart on the comparison of the estimated benefit to the allocationdecision threshold.

In some embodiments, estimating the benefit of allocating the at leastone radio resource to the wireless device 22 further includesestimating, such as via allocation unit 24, processing circuitry 34,processor 36, communication interface 28 and/or radio interface 30, thebenefit to the wireless device 22 based at least in part on at least oneof: a total of data transmitted in a downlink channel to the wirelessdevice 22; a total of time that the wireless device 22 has been activein a system of the network node 16; a downlink inactivity time; andusing a machine learning algorithm. In some embodiments, the methodfurther includes one of allocating and not allocating the at least oneradio resource to the wireless device 22 based on the determination. Insome embodiments, determining whether to allocate the at least one radioresource based at least in part on the allocation decision thresholdincludes using, such as via allocation unit 24, processing circuitry 34,processor 36, communication interface 28 and/or radio interface 30, thedynamically adjusted allocation decision threshold to control an outputof a binary classification system, the binary classification systemconfigured to determine whether to allocate the at least one radioresource to the wireless device 22. In some embodiments, the binaryclassification system may be implemented in the allocation unit 24.

FIG. 4 is a flowchart of an exemplary process in a wireless device 22for network node 16 for de-allocating resources using a dynamic decisionthreshold according to some embodiments of the present disclosure. Oneor more Blocks and/or functions and/or methods performed by WD 22 may beperformed by one or more elements of WD 22 such as by de-allocation unit26 in processing circuitry 46, processor 48, communication interface 28,radio interface 44, etc. The example method includes dynamicallyadjusting (Block S110), such as via de-allocation unit 26, processingcircuitry 46, processor 48, communication interface 28, and/or radiointerface 44, a de-allocation decision threshold. The method includesdetermining (Block S112), such as via de-allocation unit 26, processingcircuitry 46, processor 48, communication interface 28, and/or radiointerface 44, whether to de-allocate at least one radio resource basedat least in part on the de-allocation decision threshold.

In some embodiments, determining whether to de-allocate the at least oneradio resource further includes determining, such as via de-allocationunit 26, processing circuitry 46, processor 48, communication interface28, and/or radio interface 44, whether to de-allocate the at least oneradio resource for a sounding reference signal, SRS, based at least inpart on the allocation decision threshold. In some embodiments,dynamically adjusting the de-allocation decision threshold includesdynamically adjusting, such as via de-allocation unit 26, processingcircuitry 46, processor 48, communication interface 28, and/or radiointerface 44, the de-allocation decision threshold to achieve a targetde-allocation. In some embodiments, the target de-allocation includes atarget probability that an event will occur. In some embodiments, thetarget de-allocation is based at least in part on at least one of: atarget de-allocation error; a cost associated with de-allocating the atleast one radio resource; and a number of radio resource control, RRC,reconfigurations associated with de-allocating the at least one radioresource.

In some embodiments, the target probability is a predetermined targetprobability associated with at least one of: a probability ofde-allocating resources to a wireless device; and a probability ofde-allocating resources to the wireless device 22 and a same wirelessdevice 22 is subsequently allocated resources. In some embodiments,determining whether to de-allocate the at least one radio resource basedat least in part on the de-allocation decision threshold includesestimating, such as via de-allocation unit 26, processing circuitry 46,processor 48, communication interface 28, and/or radio interface 44, anon-benefit of de-allocating the at least one radio resource to thewireless device 22; comparing, such as via de-allocation unit 26,processing circuitry 46, processor 48, communication interface 28,and/or radio interface 44, the estimated non-benefit to thede-allocation decision threshold; and one of de-allocating and notde-allocating, such as via de-allocation unit 26, processing circuitry46, processor 48, communication interface 28, and/or radio interface 44,the at least one radio resource to the wireless device 22 based at leastin part on the comparison of the estimated non-benefit to thede-allocation decision threshold.

In some embodiments, dynamically adjusting the de-allocation decisionthreshold further includes increasing, such as via de-allocation unit26, processing circuitry 46, processor 48, communication interface 28,and/or radio interface 44, the de-allocation decision threshold by atleast one step up parameter when the one of the de-allocating and notde-allocating is de-allocating; and decreasing, such as viade-allocation unit 26, processing circuitry 46, processor 48,communication interface 28, and/or radio interface 44, the de-allocationdecision threshold by at least one step down parameter when the one ofthe de-allocating and not de-allocating is not deallocating.

In some embodiments, dynamically adjusting the de-allocation decisionthreshold further includes decreasing, such as via de-allocation unit26, processing circuitry 46, processor 48, communication interface 28,and/or radio interface 44, the de-allocation decision threshold by atleast one step down parameter when the one of the de-allocating and notde-allocating is de-allocating; and increasing, such as viade-allocation unit 26, processing circuitry 46, processor 48,communication interface 28, and/or radio interface 44, the de-allocationdecision threshold by at least one step up parameter and at least onestep down parameter when the one of the de-allocating and notde-allocating is not de-allocating and is further a re-allocation. Insome embodiments, a size of at least one of the step up parameter andthe step down parameter is based at least in part on a targetde-allocation.

In some embodiments, estimating the non-benefit of de-allocating the atleast one radio resource to the wireless device includes estimating,such as via de-allocation unit 26, processing circuitry 46, processor48, communication interface 28, and/or radio interface 44, thenon-benefit to the wireless device 22 based at least in part on at leastone of: a total of data transmitted in a downlink channel to thewireless device 22; a total of time that the wireless device 22 has beenactive in a system of the network node 16; a downlink inactivity time;and using a machine learning algorithm. In some embodiments, the methodfurther includes one of de-allocating and not deallocating, such as viade-allocation unit 26, processing circuitry 46, processor 48,communication interface 28, and/or radio interface 44, the at least oneradio resource to the wireless device 22 based on the determination. Insome embodiments, determining whether to de-allocate the at least oneradio resource based at least in part on the de-allocation decisionthreshold includes using, such as via de-allocation unit 26, processingcircuitry 46, processor 48, communication interface 28, and/or radiointerface 44, the dynamically adjusted de-allocation decision thresholdto control an output of a binary classification system, the binaryclassification system configured to determine whether to de-allocate theat least one radio resource to the wireless device 22. In someembodiments, the binary classification system may be implemented in thede-allocation unit 26.

Having described the general process flow of arrangements of thedisclosure and having provided examples of hardware and softwarearrangements for implementing the processes and functions of thedisclosure, the sections below provide details and examples ofarrangements for dynamic machine learning decision threshold forresource allocation/de-allocation, which may be implemented by thenetwork node 16 and/or wireless device 22.

Some embodiments provide one or more techniques for using one or moredynamic machine learning decision thresholds to allocate/de-allocateradio resources to one or more WDs 22.

Some embodiments provide for use of one or more dynamic thresholds thatare used in deciding/determining whether the user device (e.g., WD 22)should be allocated or deallocated a resource, such as a radio resource.In particular, some embodiments may use one or more of at least twothresholds, called herein as, allocTh and deallocTh, such that:

-   -   a WD 22 without a resource will be allocated a resource if the        WD's 22 estimated or predicted benefit at least meets (e.g., is        greater-than or equal-to allocTh); and/or    -   a WD 22 with a resource will be de-allocated the resource if the        WD's 22 estimated or predicted non-benefit at least meets (e.g.,        is greater-than or equal-to deallocTh).

In some embodiments, a non-benefit may be estimated separately and/orindependently from a benefit; or the non-benefit may, in someembodiments, be dependent on the benefit. For example, if a predictedbenefit is a probability, p, ranging from 0 to 1, the non-benefit may beequal to 1−p. As another example, if a predicted benefit is anon-negative score, s, ranging from 0 to any positive number e.g.,infinity, the non-benefit can be equal to −s.

In some embodiments, at least one of the thresholds, allocTh and/ordeallocTh, is updated dynamically by network node 16 to achieve apredefined target probability of a particular event, called herein asallocTarget. In one embodiment, the event may be defined as the eventthat no resource is available at a time of resource allocation (e.g.,when scheduling for a physical channel that may include a signal to beallocated a resource for that channel, such as a periodic SRS for aphysical uplink channel).

In some embodiments, the event may be defined as the event that noresource is available at the time of resource allocation while the WD's22 estimated benefit is greater-than or equal-to allocTh. In oneembodiment, allocTh is dynamically adjusted such that the probabilitythat no resource is available at the time of resource allocation, isless than a predefined target probability, allocTarget. In someembodiments, this can be achieved using a controller design in e.g.,allocation unit 24, where allocTh is updated every time an allocationdecision is made, which allocTh may be updated according to thefollowing formula, for example:

${allocTh}:=\{ \begin{matrix}{{{allocTh}\  + {upStepAlloc}},} & {{{if}{no}{resource}{is}{available}},} \\{{{allocTh} - {downStepAlloc}},} & {{otherwise},}\end{matrix} $

where upStepAlloc and downStepAlloc may be chosen as follows e.g.:

-   -   upStepAlloc: this may be a configurable parameter or variable        specifying the amount of increase in the decision threshold;        and/or    -   downStepAlloc: this may be a parameter or variable specifying        the amount of decrease in the decision threshold which may be        given by, for example:

${downStep} = {\frac{allocTarget}{1 - {allocTarget}} \times {{upStep}.}}$

In another embodiment, allocTh is dynamically adjusted by e.g.,allocation unit 24, based at least in part on an estimated benefit tothe WD 22. For example, allocTh may be dynamically adjusted such thatthe probability that no resource is available at the time of resourceallocation, while the WD's 22 estimated benefit is greater-than orequal-to allocTh, is less than a predefined target probability,allocTarget. In some embodiments, this can be achieved similar to theabove as follows, for example:

${allocTh}:=\{ \begin{matrix}{{{allocTh}\  + {upStepAlloc}},} & {{{{if}{no}{resource}{is}{available}{and}{user}{benefit}} \geq {allocTh}},} \\{{{allocTh} - {downStepAlloc}},} & {{otherwise},}\end{matrix} $

where upStepAlloc and downStepAlloc may be defined as described above.

In some embodiments, the controller design described herein may beapplied to any number of allocation decisions, such as adjusting SRSresource allocation where a control method is used to adapt the decisionthreshold for binary classification according to the load. Generally, insome embodiments, allocTh is dynamically adjusted by increasing allocThwhen there is no resource that is available (e.g., for an SRSallocation), in order to make the criteria for allocating the resourcemore restrictive so e.g., only WDs 22 with a predicted high benefit willbe allocated resources in subsequent allocation decision occasions.Conversely, allocation unit 24 decreases the allocTh when there is atleast one available resource (e.g., for SRS allocation) in order to makethe criteria of allocating resources less restrictive, so that more WDs22 can be allocated resources in subsequent allocation decisionoccasions.

It should be noted also that in some embodiments of the presentdisclosure, the network node 16 may adapt the decision threshold for abinary classification according to the load. Other controller designsmay also be applicable using the techniques disclosed herein.

In some embodiments, deallocTh is dynamically adjusted e.g., byde-allocation unit 26, based at least in part on a predefinedde-allocation target. For example, in some embodiments, deallocTh isdynamically adjusted such that the probability of de-allocatingresources at least meets or does not exceed a predefined target,deallocTarget. In some embodiments, this can be achieved by updatingdeallocTh every time a de-allocation decision is made, such as accordingto the following equation, for example:

${deallocTh}:=\{ \begin{matrix}{{{d{eallocTh}} + {upStepDealloc}},} & {{{if}{deallocation}{is}{made}},} \\{{{d{eallocTh}} - {dow{nStepDealloc}}},} & {{otherwise},}\end{matrix} $

where upStepDealloc and downStepDealloc are defined as follows, forexample:

-   -   upStepDealloc: a configurable parameter or variable specifying        the amount of increase in the decision threshold; and/or    -   downStepDealloc: the amount of decrease in the decision        threshold given by, for example:

${downStepDealloc} = {\frac{deallocTarget}{1 - {deallocTarget}} \times {{upStepDealloc}.}}$

In yet another embodiment, de-allocation unit 26 dynamically adjustsdeallocTh based at least in part on a predefined de-allocation target.For example, deallocTh may be dynamically adjusted such that theprobability of deallocating a resource for a WD 22 and then the same WD22 is allocated a resource in the future (ping-pong effect), is lessthan a predefined target, deallocTarget. In some embodiments, this canbe achieved by updating deallocTh every time a de-allocation decision orreallocation decision is made, such as, for example:

-   -   every time de-allocation decision is made:        deallocTh:=deallocTh−downStepDealloc; and    -   every time a reallocation for a WD 22 is made (i.e., the WD 22        was deallocated in the past and at a current time instance, the        WD 22 is reallocated a resource):        deallocTh:=deallocTh+upStepDealloc+downStepDealloc, where        upStepDealloc and downStepDealloc are defined the same way as in        the previous embodiment.

In yet another embodiment, deallocTh may be derived based on allocTh andthe mapping between benefit and non-benefit, while adding a hysteresisto avoid pingpong effect. For example, if a predicted benefit is anon-negative score, s, ranging from 0 to any positive number e.g.,infinity, and the non-benefit is −s, then deallocTh can be set to beequal to −allocTh+hysterisisTh, where hysterisisTh is non-negativeconfigurable static parameter. As another example, if the predictedbenefit is a probability, p, ranging from 0 to 1, and the non-benefit isequal to 1−p, then deallocTh can be set to be equal to(1-−allocTh)+hysterisisTh, where hysterisisTh is non-negativeconfigurable static parameter.

Generally, in some embodiments, deallocTh increases when de-allocationis made to make the criteria of deallocating resource more restrictiveso only user devices with predicted high non-benefit will be deallocatedresources in upcoming deallocations. Conversely, the deallocTh decreaseswhen de-allocation is not made to make the criteria of deallocatingresource less restrictive, so more user devices can be deallocatedresources in upcoming deallocations.

In yet another embodiment, deallocTarget may be set to be lower thanallocTarget to avoid a ping-pong effect.

In yet another embodiment, allocTh and/or deallocTh are bounded bypreconfigured minimum and maximum values, i.e., after each update:

allocTh:=min(max(allocTh, minAllocTh), maxAllocTh);

deallocTh:=min (max(deallocTh, minDeallocTh), maxDeallocTh).

In yet another embodiment, deallocTarget is set to be lower thanallocTarget.

In yet another embodiment, deallocTarget and allocTarget, are adjustedto control a cost of allocating and/or deallocating resources. In someembodiments, the cost can be e.g., a number of RRC reconfigurations forallocating and/or deallocating the resources. In general, someembodiments of the present disclosure may be applicable for any benefitand non-benefit estimators. In one example, the benefit estimator andnon-benefit estimators are derived directly from observed features. Forexample, the benefit can be equal to a total data that was transmittedin the downlink for the WD 22, or the downlink inactivity time definedas the difference between the time the benefit is estimated, and thelast time downlink data transmission occurred, the time the WD 22 wasactive, or the total time the WD 22 is in the system (e.g., total timeWD 22 was in a cell provided by a gNB), or any other feature that may beused to measure a WD 22 benefit. In another example, the benefitestimator and/or non-benefit estimator can be based on machine learningalgorithms that use any set of observed features as inputs to a machinelearning model such as Logistic regression, random-forest, decisiontrees, neural networks, support vector machines, K-nearest-neighbors,long-short-term memory networks, etc. In some embodiments, the benefitestimator may be implemented in the allocation unit 24 or otherwise inthe network node 16. In some embodiments, the non-benefit estimator maybe implemented in the de-allocation unit 26 or otherwise in the networknode 16.

One example process of resource allocation/de-allocation (which may beperformed by network node 16, such as via e.g., allocation unit 24and/or de-allocation unit 26) including dynamic decision thresholds isillustrated in the flowchart depicted in FIG. 5 . For a given WD 22, asinput in step S200 for example, the network node 16 may determinewhether or not the WD 22 has a resource (e.g., whether the WD 22 isallocated a periodic SRS) in step S202.

If the user device has no resource, in step S204, network node 16 maydetermine whether there are any available resources. If there are noavailable resources, in step S206, the network node 16 will not allocateto the WD 22 any resource and the allocation decision threshold,allocTh, is adjusted, such as, increased by e.g., upStepAlloc. If thereis an available resource, in step S208, network node 16 may estimate thebenefit that the WD 22 may have by obtaining a resource. In step S210,network node 16 may compare the estimated benefit to the allocTh. Forexample, if the estimated benefit is lower than the allocTh, the WD 22may not be allocated a resource in step S212; otherwise, the WD 22 maybe allocated a resource in step S214. Since there is a resourceavailable, in step S216, network node 16 may adjust the allocationdecision threshold, allocTh, e.g., decreasing by downStepAlloc.

On the other, in step S202, if the WD 22 has a resource, the networknode 16 may estimate the non-benefit that WD may have by keeping theresource in step S218. In step S220, network node 16 may compare theestimated non-benefit to the de-allocation decision threshold,deallocTh. For example, if the estimated non-benefit is lower than thedeallocTh, the process may proceed to step S222 where the resource willnot be deallocated from the WD and the deallocTh is adjusted, such asdecreased by e.g., downStepDealloc. If the estimated non-benefit isgreater than or equal-to the deallocTh, the process may proceed to stepS224 where the resource will be deallocated from the WD 22 and thedeallocTh is adjusted, such as increased by e.g., upStepDealloc.

As will be appreciated by one of skill in the art, the conceptsdescribed herein may be embodied as a method, data processing system,computer program product and/or computer storage media storing anexecutable computer program. Accordingly, the concepts described hereinmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment or an embodiment combining software and hardwareaspects all generally referred to herein as a “circuit” or “module.” Anyprocess, step, action and/or functionality described herein may beperformed by, and/or associated to, a corresponding module, which may beimplemented in software and/or firmware and/or hardware. Furthermore,the disclosure may take the form of a computer program product on atangible computer usable storage medium having computer program codeembodied in the medium that can be executed by a computer. Any suitabletangible computer readable medium may be utilized including hard disks,CD-ROMs, electronic storage devices, optical storage devices, ormagnetic storage devices.

Some embodiments are described herein with reference to flowchartillustrations and/or block diagrams of methods, systems and computerprogram products. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer (to therebycreate a special purpose computer), special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

These computer program instructions may also be stored in a computerreadable memory or storage medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks mayoccur out of the order noted in the operational illustrations. Forexample, two blocks shown in succession may in fact be executedsubstantially concurrently or the blocks may sometimes be executed inthe reverse order, depending upon the functionality/acts involved.Although some of the diagrams include arrows on communication paths toshow a primary direction of communication, it is to be understood thatcommunication may occur in the opposite direction to the depictedarrows.

Computer program code for carrying out operations of the conceptsdescribed herein may be written in an object oriented programminglanguage such as Java® or C++. However, the computer program code forcarrying out operations of the disclosure may also be written inconventional procedural programming languages, such as the “C”programming language. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer. In the latter scenario, theremote computer may be connected to the user's computer through a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider).

Many different embodiments have been disclosed herein, in connectionwith the above description and the drawings. It will be understood thatit would be unduly repetitious and obfuscating to literally describe andillustrate every combination and subcombination of these embodiments.Accordingly, all embodiments can be combined in any way and/orcombination, and the present specification, including the drawings,shall be construed to constitute a complete written description of allcombinations and subcombinations of the embodiments described herein,and of the manner and process of making and using them, and shallsupport claims to any such combination or subcombination.

It will be appreciated by persons skilled in the art that theembodiments described herein are not limited to what has beenparticularly shown and described herein above. In addition, unlessmention was made above to the contrary, it should be noted that all ofthe accompanying drawings are not to scale. A variety of modificationsand variations are possible in light of the above teachings withoutdeparting from the scope of the following claims.

1. A method implemented in a network node, the method comprising:dynamically adjusting an allocation decision threshold; and determiningwhether to allocate at least one radio resource based at least in parton the allocation decision threshold.
 2. The method of claim 1, whereindetermining whether to allocate the at least one radio resource furthercomprises: determining whether to allocate the at least one radioresource for a sounding reference signal, SRS, based at least in part onthe allocation decision threshold.
 3. The method of claim 1, whereindynamically adjusting the allocation decision threshold comprises:dynamically adjusting the allocation decision threshold to achieve atarget allocation.
 4. The method of claim 1, wherein the targetallocation comprises a target probability that an event will occur. 5.The method of claim 4, wherein the target allocation is based at leastin part on at least one of: a target allocation error; a cost associatedwith allocating the at least one radio resource; and a number of radioresource control, RRC, reconfigurations associated with allocating theat least one radio resource.
 6. The method of claim 4, wherein thetarget probability is a predetermined target probability that the eventwill occur, the event being one of: that a radio resource is unavailablefor allocation to a wireless device; and that the radio resource isunavailable for allocation to the wireless device while an estimatedbenefit to the wireless device is greater than or equal to theallocation decision threshold.
 7. The method of claim 1, whereindynamically adjusting the allocation decision threshold comprises:determining whether there is at least one radio resource that isavailable for the allocation to a wireless device; increasing theallocation decision threshold by at least one step up parameter whenthere is at least one radio resource that is available for theallocation to the wireless device; and decreasing the allocationdecision threshold by at least one step down parameter when there is anunavailability of at least one radio resource for the allocation to thewireless device.
 8. The method of claim 1, wherein dynamically adjustingthe allocation decision threshold comprises: determining whether thereis at least one radio resource that is available for the allocation to awireless device; increasing the allocation decision threshold by atleast one step up parameter when there is at least one radio resourcethat is available for the allocation to the wireless device and anestimated benefit to the wireless device is greater than or equal to theallocation decision threshold; and decreasing the allocation decisionthreshold by at least one step down parameter when there is anunavailability of at least one radio resource for the allocation to thewireless device.
 9. The method of claim 7, wherein a size of at leastone of the step up parameter and the step down parameter is based atleast in part on a target allocation.
 10. The method of claim 1, whereindetermining whether to allocate the at least one radio resource based atleast in part on the allocation decision threshold comprises: estimatinga benefit of allocating the at least one radio resource to a wirelessdevice; comparing the estimated benefit to the allocation decisionthreshold; and one of allocating and not allocating the at least oneradio resource to the wireless device based at least in part on thecomparison of the estimated benefit to the allocation decisionthreshold.
 11. The method of claim 10, wherein estimating the benefit ofallocating the at least one radio resource to the wireless devicefurther comprises: estimating the benefit to the wireless device basedat least in part on at least one of: a total of data transmitted in adownlink channel to the wireless device; a total of time that thewireless device has been active in a system of the network node; adownlink inactivity time; and using a machine learning algorithm. 12.The method of claim 1, further comprising: one of allocating and notallocating the at least one radio resource to a wireless device based onthe determination.
 13. The method of claim 1, wherein determiningwhether to allocate the at least one radio resource based at least inpart on the allocation decision threshold comprises: using thedynamically adjusted allocation decision threshold to control an outputof a binary classification system, the binary classification systemconfigured to determine whether to allocate the at least one radioresource to a wireless device.
 14. A method implemented in a networknode, the method comprising: dynamically adjusting a de-allocationdecision threshold; and determining whether to de-allocate at least oneradio resource based at least in part on the de-allocation decisionthreshold.
 15. The method of claim 14, wherein determining whether todeallocate the at least one radio resource further comprises:determining whether to de-allocate the at least one radio resource for asounding reference signal, SRS, based at least in part on the allocationdecision threshold.
 16. The method of claim 14, wherein dynamicallyadjusting the de-allocation decision threshold comprises: dynamicallyadjusting the de-allocation decision threshold to achieve a targetde-allocation.
 17. The method of claim 14, wherein the targetde-allocation comprises a target probability that an event will occur.18. The method of claim 17, wherein the target de-allocation is based atleast in part on at least one of: a target de-allocation error; a costassociated with de-allocating the at least one radio resource; and anumber of radio resource control, RRC, reconfigurations associated withde-allocating the at least one radio resource.
 19. The method of claim17, wherein the target probability is a predetermined target probabilityassociated with at least one of: a probability of de-allocatingresources to a wireless device; and a probability of de-allocatingresources to the wireless device and a same wireless device issubsequently allocated resources.
 20. The method of claim 14, whereindetermining whether to de-allocate the at least one radio resource basedat least in part on the de-allocation decision threshold comprises:estimating a non-benefit of de-allocating the at least one radioresource to a wireless device; comparing the estimated non-benefit tothe de-allocation decision threshold; and one of de-allocating and notde-allocating the at least one radio resource to the wireless devicebased at least in part on the comparison of the estimated non-benefit tothe de-allocation decision threshold.
 21. The method of claim 20,wherein dynamically adjusting the de-allocation decision thresholdfurther comprises: increasing the de-allocation decision threshold by atleast one step up parameter when the one of the de-allocating and notde-allocating is de-allocating; and decreasing the de-allocationdecision threshold by at least one step down parameter when the one ofthe de-allocating and not de-allocating is not de-allocating.
 22. Themethod of claim 20, wherein dynamically adjusting the de-allocationdecision threshold further comprises: decreasing the de-allocationdecision threshold by at least one step down parameter when the one ofthe de-allocating and not de-allocating is de-allocating; and increasingthe de-allocation decision threshold by at least one step up parameterand at least one step down parameter when the one of the de-allocatingand not de-allocating is not deallocating and is further are-allocation.
 23. The method of claim 21, wherein a size of at leastone of the step up parameter and the step down parameter is based atleast in part on a target de-allocation.
 24. The method of claim 20,wherein estimating the non-benefit of de-allocating the at least oneradio resource to the wireless device comprises: estimating thenon-benefit to the wireless device based at least in part on at leastone of: a total of data transmitted in a downlink channel to thewireless device; a total of time that the wireless device has beenactive in a system of the network node; a downlink inactivity time; andusing a machine learning algorithm.
 25. The method of claim 14, furthercomprising: one of de-allocating and not de-allocating the at least oneradio resource to a wireless device based on the determination.
 26. Themethod of claim 14, wherein determining whether to de-allocate the atleast one radio resource based at least in part on the de-allocationdecision threshold comprises: using the dynamically adjustedde-allocation decision threshold to control an output of a binaryclassification system, the binary classification system configured todetermine whether to de-allocate the at least one radio resource to awireless device.
 27. A network node comprising processing circuitry, theprocessing circuitry configured to cause the network node to:dynamically adjust an allocation decision threshold; and determinewhether to allocate at least one radio resource based at least in parton the allocation decision threshold. 28.-39. (canceled)
 40. A networknode comprising processing circuitry, the processing circuitryconfigured to cause the network node to: dynamically adjust ade-allocation decision threshold; and determine whether to de-allocateat least one radio resource based at least in part on the de-allocationdecision threshold. 41.-52. (canceled)